The problem
Customer calls contain useful evidence about recurring issues, sentiment, and process failures. That evidence is hard to compare while it remains in long transcripts. Operations teams need structured data they can query and explore without reading each conversation.
The approach
- Stored transcripts in Databricks with access governed through Unity Catalog.
- Built a daily batch pipeline to ingest and analyse transcripts.
- Used structured LLM outputs to classify calls, assess sentiment, and extract operational signals.
- Converted the results into SQL tables for analysis and reporting.
- Built a Databricks app for Operations teams to explore trends and customer issues.
What changed
- Moved the workflow into regular operational use with daily transcript processing.
- Made customer issues, sentiment, and process signals available as structured, queryable data.
- Reduced reliance on manual transcript sampling when investigating customer conversations.